Statistical analysis of total column ozone during three recent solar cycles over India. (December 2018)

• Record Type: Journal Article
• Authors: Potdar, Swapnil S.; Nade, D.P.; Pawar, R.P.; Jeni Victor, N.; Nikte, S.S.; Chavan, G.A.; Taori, A.; Siingh, Devendraa
• Is Part Of: Journal of atmospheric and solar-terrestrial physics. Volume 181(2018)Part A
• Page Start: 44
• Page End: 54
• Publication Date: 2018-12
• Subjects: Total column ozone -- Linear trend -- Aerosol loading and anthropogenic emission -- Sunspot number -- F10.7 solar flux -- Solar cycle; Geophysics -- Periodicals; Atmospheric physics -- Periodicals; Géophysique -- Périodiques; Météorologie physique -- Périodiques; Electronic journals; 551.51
• DOI: 10.1016/j.jastp.2018.10.015
• Languages: English
• ISSNs: 1364-6826

Abstract

Abstract: Total column ozone (TCO) distribution and its variation over the Indian region at different stations for the period of about 30 years from 1986 to 2015 are studied. TCO data is taken from the merged ozone data set (MOD) overpass data for 15 different stations over India. The average correlation between TCO and solar proxies such as sunspot number and F10.7 cm solar flux is more than 0.5. We further divided the time series of TCO according to solar cycle as 22 nd solar cycle (September 1986 to July 1996), 23 rd solar cycle (August 1996 to November 2008) and 24 th solar cycle (December 2008 to December 2015) (on going cycle) for a period of 1986–2015. Herein, for the long term trend analysis of TCO, we have removed the seasonal effect by the deseasonalization process, the effects of solar activities, stratospheric waves (quasi-biennial-oscillation-QBO and El Niño–Southern Oscillation-ENSO) by the multifunction linear regression method (MLR). We have compared both the linear trends in TCO which are calculated by the simple linear regression (SLR) and deseasonalised multifunction linear regression (DMLR) analysis. It is found that the direction of the trend in 22 nd and the 23 rd solar cycle is similar while, it is opposite in the 24 th solar cycle. We observed a more negative trend in the 22 nd solar cycle and less negative trend in the 23 rd solar cycle while the trend is positive in the 24 th solar cycle. The results indicate that after the DMLR process, the trend values are decreased by a large factor. Therefore, it is found that the role of natural variability is more than that of the ozone depleting substances (ODS) on long term variability in TCO over India. This statistical analysis provides better analysis of trend variation in TCO series over India. The main objective of this work is to analyze the variations in trend in the TCO with respect to the recent three solar cycles. Highlights: The paper, for the first time, demonstrated the solar cycle wise trend distribution and variation of TCO over India. This study focused on the period of recent three solar cycles (from 1986 to 2015). The statistical analysis adopted deseasonalised and multifunction linear regression and Simple linear regression. Trend values for TCO by SLR and DMLR are compared.